Key to the successful deployment of autonomous vehicles envisioned under future defense system programs is the ability for automated target identification. For true vehicular autonomy, this automation of the decision-making function should be extended to include incompletely known problem environments, perhaps with less than perfect supervisory information being available for training and design of the target recognition processor. Driven by these real-world requirements, this study develops a panoramic view of the entire learning environment scenario and delineates the most suitable techniques/algorithms available for addressing the identification problem under this spectrum of environments. This brings into focus the state-of-the-art in this field and serves as an information base for defining the directions for further developments required to meet the needs of autonomous vehicle missions.